{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-01-02</td>\n",
       "      <td>0.196417</td>\n",
       "      <td>0.199083</td>\n",
       "      <td>0.192333</td>\n",
       "      <td>0.192333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.195250</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.198583</td>\n",
       "      <td>575292000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-01-06</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.209417</td>\n",
       "      <td>0.197083</td>\n",
       "      <td>0.206667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.204333</td>\n",
       "      <td>0.209500</td>\n",
       "      <td>0.202917</td>\n",
       "      <td>0.208500</td>\n",
       "      <td>673032000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>0.211083</td>\n",
       "      <td>0.212083</td>\n",
       "      <td>0.207250</td>\n",
       "      <td>0.209250</td>\n",
       "      <td>433752000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close       Volume\n",
       "0  2004-01-02  0.196417  0.199083  0.192333  0.192333          NaN\n",
       "1         NaN  0.195250  0.199917  0.193500  0.198583  575292000.0\n",
       "2  2004-01-06  0.198000  0.209417  0.197083  0.206667          NaN\n",
       "3         NaN  0.204333  0.209500  0.202917  0.208500  673032000.0\n",
       "4  2004-01-08  0.211083  0.212083  0.207250  0.209250  433752000.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"nvidia_stock_prices.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.195250</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.198583</td>\n",
       "      <td>575292000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.204333</td>\n",
       "      <td>0.209500</td>\n",
       "      <td>0.202917</td>\n",
       "      <td>0.208500</td>\n",
       "      <td>673032000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>0.211083</td>\n",
       "      <td>0.212083</td>\n",
       "      <td>0.207250</td>\n",
       "      <td>0.209250</td>\n",
       "      <td>433752000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>0.207833</td>\n",
       "      <td>0.214833</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>0.212250</td>\n",
       "      <td>766584000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>0.213000</td>\n",
       "      <td>0.215333</td>\n",
       "      <td>0.211000</td>\n",
       "      <td>0.214667</td>\n",
       "      <td>541980000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close       Volume\n",
       "1         NaN  0.195250  0.199917  0.193500  0.198583  575292000.0\n",
       "3         NaN  0.204333  0.209500  0.202917  0.208500  673032000.0\n",
       "4  2004-01-08  0.211083  0.212083  0.207250  0.209250  433752000.0\n",
       "5  2004-01-09  0.207833  0.214833  0.206167  0.212250  766584000.0\n",
       "6  2004-01-12  0.213000  0.215333  0.211000  0.214667  541980000.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除 Volume 值为空的一行数据\n",
    "df.dropna(subset = [\"Volume\"],inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>time</td>\n",
       "      <td>0.195250</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.198583</td>\n",
       "      <td>575292000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>time</td>\n",
       "      <td>0.204333</td>\n",
       "      <td>0.209500</td>\n",
       "      <td>0.202917</td>\n",
       "      <td>0.208500</td>\n",
       "      <td>673032000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>0.211083</td>\n",
       "      <td>0.212083</td>\n",
       "      <td>0.207250</td>\n",
       "      <td>0.209250</td>\n",
       "      <td>433752000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>0.207833</td>\n",
       "      <td>0.214833</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>0.212250</td>\n",
       "      <td>766584000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>0.213000</td>\n",
       "      <td>0.215333</td>\n",
       "      <td>0.211000</td>\n",
       "      <td>0.214667</td>\n",
       "      <td>541980000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close       Volume\n",
       "1        time  0.195250  0.199917  0.193500  0.198583  575292000.0\n",
       "3        time  0.204333  0.209500  0.202917  0.208500  673032000.0\n",
       "4  2004-01-08  0.211083  0.212083  0.207250  0.209250  433752000.0\n",
       "5  2004-01-09  0.207833  0.214833  0.206167  0.212250  766584000.0\n",
       "6  2004-01-12  0.213000  0.215333  0.211000  0.214667  541980000.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据中缺失 Date 的填充 00000000\n",
    "df[\"Date\"].fillna(value=\"time\",inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>time</td>\n",
       "      <td>0.195250</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.198583</td>\n",
       "      <td>575292000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>0.211083</td>\n",
       "      <td>0.212083</td>\n",
       "      <td>0.207250</td>\n",
       "      <td>0.209250</td>\n",
       "      <td>433752000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>0.207833</td>\n",
       "      <td>0.214833</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>0.212250</td>\n",
       "      <td>766584000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>0.213000</td>\n",
       "      <td>0.215333</td>\n",
       "      <td>0.211000</td>\n",
       "      <td>0.214667</td>\n",
       "      <td>541980000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004-01-13</td>\n",
       "      <td>0.213583</td>\n",
       "      <td>0.215667</td>\n",
       "      <td>0.201333</td>\n",
       "      <td>0.203583</td>\n",
       "      <td>865800000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close       Volume\n",
       "1        time  0.195250  0.199917  0.193500  0.198583  575292000.0\n",
       "4  2004-01-08  0.211083  0.212083  0.207250  0.209250  433752000.0\n",
       "5  2004-01-09  0.207833  0.214833  0.206167  0.212250  766584000.0\n",
       "6  2004-01-12  0.213000  0.215333  0.211000  0.214667  541980000.0\n",
       "7  2004-01-13  0.213583  0.215667  0.201333  0.203583  865800000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除文件中的重复值\n",
    "df.drop_duplicates(subset=[\"Date\"],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>0.195250</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.198583</td>\n",
       "      <td>575292000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>0.211083</td>\n",
       "      <td>0.212083</td>\n",
       "      <td>0.207250</td>\n",
       "      <td>0.209250</td>\n",
       "      <td>433752000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>0.207833</td>\n",
       "      <td>0.214833</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>0.212250</td>\n",
       "      <td>766584000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>0.213000</td>\n",
       "      <td>0.215333</td>\n",
       "      <td>0.211000</td>\n",
       "      <td>0.214667</td>\n",
       "      <td>541980000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004-01-13</td>\n",
       "      <td>0.213583</td>\n",
       "      <td>0.215667</td>\n",
       "      <td>0.201333</td>\n",
       "      <td>0.203583</td>\n",
       "      <td>865800000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close       Volume\n",
       "1  2004-01-01  0.195250  0.199917  0.193500  0.198583  575292000.0\n",
       "4  2004-01-08  0.211083  0.212083  0.207250  0.209250  433752000.0\n",
       "5  2004-01-09  0.207833  0.214833  0.206167  0.212250  766584000.0\n",
       "6  2004-01-12  0.213000  0.215333  0.211000  0.214667  541980000.0\n",
       "7  2004-01-13  0.213583  0.215667  0.201333  0.203583  865800000.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数值修改和替换\n",
    "df[\"Date\"].replace(\"time\",\"2004-01-01\",inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>2004-01-01</td>\n",
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       "      <td>0.199917</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.198583</td>\n",
       "      <td>575292000.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>0.211083</td>\n",
       "      <td>0.212083</td>\n",
       "      <td>0.207250</td>\n",
       "      <td>0.209250</td>\n",
       "      <td>433752000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>0.207833</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>0.213000</td>\n",
       "      <td>0.215333</td>\n",
       "      <td>0.211000</td>\n",
       "      <td>0.214667</td>\n",
       "      <td>541980000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004-01-13</td>\n",
       "      <td>0.213583</td>\n",
       "      <td>0.215667</td>\n",
       "      <td>0.201333</td>\n",
       "      <td>0.203583</td>\n",
       "      <td>865800000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close        Total\n",
       "1  2004-01-01  0.195250  0.199917  0.193500  0.198583  575292000.0\n",
       "4  2004-01-08  0.211083  0.212083  0.207250  0.209250  433752000.0\n",
       "5  2004-01-09  0.207833  0.214833  0.206167  0.212250  766584000.0\n",
       "6  2004-01-12  0.213000  0.215333  0.211000  0.214667  541980000.0\n",
       "7  2004-01-13  0.213583  0.215667  0.201333  0.203583  865800000.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 更改列名称\n",
    "df.rename(columns={\"Volume\":\"Total\"},inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.199917</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.198583</td>\n",
       "      <td>575292000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>0</td>\n",
       "      <td>0.212083</td>\n",
       "      <td>0.207250</td>\n",
       "      <td>0.209250</td>\n",
       "      <td>433752000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0.214833</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>0.212250</td>\n",
       "      <td>766584000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.215333</td>\n",
       "      <td>0.211000</td>\n",
       "      <td>0.214667</td>\n",
       "      <td>541980000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004-01-13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.215667</td>\n",
       "      <td>0.201333</td>\n",
       "      <td>0.203583</td>\n",
       "      <td>865800000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Open      High       Low     Close        Total\n",
       "1  2004-01-01     0  0.199917  0.193500  0.198583  575292000.0\n",
       "4  2004-01-08     0  0.212083  0.207250  0.209250  433752000.0\n",
       "5  2004-01-09     0  0.214833  0.206167  0.212250  766584000.0\n",
       "6  2004-01-12     0  0.215333  0.211000  0.214667  541980000.0\n",
       "7  2004-01-13     0  0.215667  0.201333  0.203583  865800000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 更改数据格式\n",
    "df[\"Open\"] = df[\"Open\"].astype(\"int\")\n",
    "df.head()\n",
    "# df[\"Date\"] = pd.to_datetime(df[\"Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 4320x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制折线图\n",
    "df = df.head(60)\n",
    "plt.figure(figsize = (60,8))\n",
    "plt.plot(df[\"Date\"],df[\"Close\"],marker=\"o\")\n",
    "plt.title(\"stock_price\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Close\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制柱状图\n",
    "df = df.head(30)\n",
    "plt.figure(figsize = (30,8))\n",
    "plt.bar(df[\"Date\"],df[\"Total\"],color = \"blue\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Total\")\n",
    "plt.title(\"Volume Picture\")\n",
    "plt.xticks(rotation=45)  # 将 X 轴上的标签逆时针旋转 45 度\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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